Results 21 to 30 of about 9,899,096 (370)
Xception: Deep Learning with Depthwise Separable Convolutions [PDF]
Computer Vision and Pattern Recognition, 2016 We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution)
François Chollet
semanticscholar +1 more source
Deep Learning Face Attributes in the Wild [PDF]
IEEE International Conference on Computer Vision, 2014 Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild.
Ziwei Liu +3 more
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Alzheimer’s Research & Therapy, 2023 Background Developing a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer’s disease or the clinical setting.
Noriyuki Kimura +9 more
doaj +1 more source
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [PDF]
Computer Vision and Pattern Recognition, 2016 Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In
C. Qi, Hao Su, Kaichun Mo, L. Guibas
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Understanding deep learning (still) requires rethinking generalization
Communications of the ACM, 2021 Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small gap between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family or to the ...
Chiyuan Zhang +4 more
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Unconstrained neighbor selection for minimum reconstruction error-based K-NN classifiers
Complex & Intelligent Systems, 2023 It is essential to define more convincing and applicable classifiers for small datasets. In this paper, a minimum reconstruction error-based K-nearest neighbors (K-NN) classifier is proposed. We propose a new neighbor selection method.
Rassoul Hajizadeh
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The Limitations of Deep Learning in Adversarial Settings [PDF]
European Symposium on Security and Privacy, 2015 Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks.
Nicolas Papernot +5 more
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Epilepsia Open, 2023 Objective As of 2022, 36 anti‐seizure medications (ASMs) have been licensed for the treatment of epilepsy, however, adverse effects (AEs) are commonly reported.
Kazuyuki Fukushima +14 more
doaj +1 more source
Wide & Deep Learning for Recommender Systems [PDF]
DLRS@RecSys, 2016 Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs.
Heng-Tze Cheng +15 more
semanticscholar +1 more source
Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras [PDF]
, 2018 Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image,
Benito Picazo, Jesús +4 more
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